1 Load Libraries

library(knitr)
library(readxl)
library(openxlsx)
library(dplyr)
library(tidyr)
library(DescTools)
library(DT)
library(ggplot2)
library(ggiraph)
library(gridExtra)
library(plotly)

LibrariesVersions <- data.frame(
  LIBRARY = c("KNITR", "READXL", "OPENXLSX", "DPLYR", "TIDYR", 
              "DESCTOOLS", "DT", "GGPLOT2", "GGIRAPH", "GRIDEXTRA", "PLOTLY"), 
  VERSION = c(as.character(packageVersion("knitr")), as.character(packageVersion("readxl")), 
              as.character(packageVersion("openxlsx")), as.character(packageVersion("dplyr")), 
              as.character(packageVersion("tidyr")), as.character(packageVersion("DescTools")), 
              as.character(packageVersion("DT")), as.character(packageVersion("ggplot2")), 
              as.character(packageVersion("ggiraph")), as.character(packageVersion("gridExtra")), 
              as.character(packageVersion("plotly"))))

datatable(LibrariesVersions, options = list(scrollX = TRUE, autoWidth = TRUE), 
          caption = paste("Libraries Versions | R",  
                          gsub("^R version\\s*|\\s*\\(\\d{4}-\\d{2}-\\d{2} ucrt\\)", "", R.version.string)),
          style = "bootstrap", rownames = FALSE) %>%
          formatStyle(columns = 1, textAlign = "left") %>% formatStyle(columns = 2, textAlign = "center")

2 ISO 3166-1 and M49 Codes

ISO 3166-1 is an international standard from International OrGanization for Standardization (ISO), with 3 codes:

ALPHA-2 CODE (ISO2)
ALPHA-3 CODE (ISO3)
NUMERIC CODE

NUMERIC CODE \(\equiv\) M49 CODE from United Nations Statistic Division (UNSD). ISO 3166-1 is available for autonomous countries or territories, while M49 is also available for world or continents.

How to obtain a code? Be a United Nations (UN) or a UN Specialized AGencies (FAO | World Bank | WHO | …) Member State.

There are more NUMERIC/M49 CODE (249) than countries in UN (198).

3 International MiGration Stock Data

Data are taken from UN | PoPulation Division. Estimates of the number (or “stock”) of international miGrants (1990-2020) are available for 235 countries/areas of the world. The international miGrant population corresponds to the total number of miGrants present in a Given countrY at a Given moment in time (foreiGn-born). Tourists are not counted amonG miGrants.

Data are orGanized on the basis of Residence-Birth pairs accordinG to certain classifications or countries/areas:

6 Continental REGIONs with 22 Sub-REGIONs and Intermediate REGIONs:
- Africa
- Asia
- Europe
- Latin America and the Caribbean
- Northern America
- Oceania
8 GeoGraphic REGIONs based on the Sustainable Development Goals (SDG) Classification from UN:
- Australia and New Zealand
- Central and Southern Asia
- Eastern and South-Eastern Asia
- Europe and Northern America
- Latin America and the Caribbean
- Northern Africa and Western Asia
- Oceania (Australia and New Zealand excluded)
- Sub-Saharan Africa
2 Development Levels:
- More Developed REGIONs:
  - Australia and New Zealand and Japan
  - Europe
  - Northern America
- Less Developed REGIONs:
  - Africa
  - Asia (Japan excluded)
  - Latin America and the Caribbean
  - Oceania (Australia and New Zealand excluded)
3 Development Levels (137/232) from OHRLLS:
- Least Developed Countries (LDC | 47)
- Land-Locked DevelopinG Countries (LLDC | 32)
- Small Island DevelopinG States (SIDS | 58)
3/4 Income Levels based on Gross National Income (GNI | World Bank 2020):
- HiGh-Income Countries
- Middle-Income Countries
  - Upper-Middle-Income Countries
  - Lower-Middle-Income Countries
- Low-Income Countries

Remark: Income Levels are not available for all countries/areas.

3.1 Data Exploration

International MiGrant Stocks (IMS) in the World (Residence) born in Europe and Northern America (Birth) are shown in this table. We have estimates from 1990 to 2020 (Year).

#Load XLSX File
IMS_Total <- read_xlsx(
  "../Données/IMS_from_ONU/ims_sex_residence_and_birth_2020.xlsx", sheet = "Table 1", skip = 8) %>%
  select(2, 4, 6:14) %>%
  rename(Residence = 1, ResidenceCode = 2, Birth = 3, BirthCode = 4, 
         `1990` = 5, `1995` = 6, `2000` = 7, `2005` = 8, `2010` = 9, `2015` = 10, `2020` = 11)
#One line (with all Years) for each Residence-Birth to one line/Year for each Residence-Birth
IMS_Total <- pivot_longer(IMS_Total, cols = 5:11) %>% rename(Year = 5, IMS_Total = 6)

#IMS_Male
IMS_Male <- read_xlsx(
  "../Données/IMS_from_ONU/ims_sex_residence_and_birth_2020.xlsx", sheet = "Table 1", skip = 8) %>% 
  select(2, 4, 6:7, 15:21) %>% 
  rename(Residence = 1, ResidenceCode = 2, Birth = 3, BirthCode = 4, 
         `1990` = 5, `1995` = 6, `2000` = 7, `2005` = 8, `2010` = 9, `2015` = 10, `2020` = 11)
IMS_Male <- pivot_longer(IMS_Male, cols = 5:11) %>% rename(Year = 5, IMS_Male = 6)

#IMS_Female
IMS_Female <- read_xlsx(
  "../Données/IMS_from_ONU/ims_sex_residence_and_birth_2020.xlsx", sheet = "Table 1", skip = 8) %>% 
  select(2, 4, 6:7, 22:28) %>% 
  rename(Residence = 1, ResidenceCode = 2, Birth = 3, BirthCode = 4, 
         `1990` = 5, `1995` = 6, `2000` = 7, `2005` = 8, `2010` = 9, `2015` = 10, `2020` = 11)
IMS_Female <- pivot_longer(IMS_Female, cols = 5:11) %>% rename(Year = 5, IMS_Female = 6)

#Combine IMS_Total | IMS_Male | IMS_Female
IMS <- cbind(IMS_Total, IMS_Male[length(IMS_Male)], IMS_Female[length(IMS_Female)])

#Example: World-(Europe and NA)
kable(IMS[57:63, c(1, 3, 5:8)], row.names = FALSE, align = c("l", "l", "c", "c", "c", "c"),
      caption = "World's MiGration Stocks born in Europe and Northern America (1990-2020)")

World’s MiGration Stocks born in Europe and Northern America (1990-2020)
Residence	Birth	Year	IMS_Total	IMS_Male	IMS_Female
WORLD	Europe and Northern America	1990	50532413	23704534	26827879
WORLD	Europe and Northern America	1995	51582164	24148758	27433406
WORLD	Europe and Northern America	2000	52877703	24676104	28201599
WORLD	Europe and Northern America	2005	54849090	25682798	29166292
WORLD	Europe and Northern America	2010	58681104	27337252	31343852
WORLD	Europe and Northern America	2015	60592402	28229706	32362696
WORLD	Europe and Northern America	2020	67601621	31770385	35831236

This was an example between World-(Europe and NA). Data are also available for more areas/countries (282).

#Residence(s) in XLSX
ResidencesIMS <- unique(IMS[c("Residence", "ResidenceCode")])
datatable(ResidencesIMS, options = list(scrollX = TRUE, autoWidth = TRUE), 
          caption = "Residences in IMS DATA", style = "bootstrap", rownames = FALSE) %>%
  formatStyle(columns = 1, textAlign = "left", `text-transform` = "uppercase") %>% 
  formatStyle(columns = 2, textAlign = "center")

A few minor alterations have been made to Residence and Birth columns.

#Columns: Residence | Birth
IMS.DATA <- IMS %>%
  mutate_if(is.character, ~case_when(
    . == "Oceania (excluding Australia and New Zealand)" ~ "Oceania (Australia and New Zealand excluded)",
    . == "Developed regions" ~ "More Developed Countries",
    . == "Less developed regions" ~ "Less Developed Countries",
    . == "Less developed regions, excluding least developed countries" ~ 
      "Less Developed Countries (Least Developed Countries excluded)",
    . == "Less developed regions, excluding China" ~ "Less Developed Countries (China excluded)",
    . == "Least developed countries" ~ "Least Developed Countries (LDC)",
    . == "Land-locked Developing Countries (LLDC)" ~ "Land-Locked Developing Countries (LLDC)",
    . == "Small island developing States (SIDS)" ~ "Small Island Developing States (SIDS)",
    . == "High-income countries" ~ "High-Income Countries",
    . == "Middle-income countries" ~ "Middle-Income Countries",
    . == "Upper-middle-income countries" ~ "Upper-Middle-Income Countries",
    . == "Lower-middle-income countries" ~ "Lower-Middle-Income Countries",
    . == "Low-income countries" ~ "Low-Income Countries",
    . == "Bolivia (Plurinational State of)" ~ "Bolivia",
    . == "China, Hong Kong SAR*" ~ "Hong Kong SAR",
    . == "China, Macao SAR*" ~ "Macao SAR",
    . == "China, Taiwan Province of China*" ~ "Taiwan (Province of China)",
    . == "Iran (Islamic Republic of)" ~ "Iran",
    . == "Micronesia (Fed. States of)" ~ "Micronesia",
    . == "Saint Helena*" ~ "Saint Helena, Ascension and Tristan da Cunha",
    . == "Saint Martin (French part)*" ~ "Saint Martin",
    . == "Sint Maarten (Dutch part)*" ~ "Sint Maarten",
    . == "Turkey" ~ "Türkiye",
    . == "United Kingdom*" ~ "United Kingdom of Great Britain and Northern Ireland",
    . == "Venezuela (Bolivarian Republic of)" ~ "Venezuela", TRUE ~ .)) %>%
  mutate(across(c(Residence, Birth), ~gsub("\\*", "", .)))

3.2 Visualization (Continental REGIONs)

Remark: STOCKs constitute a fraction of the information, we need to know SHAREs.

We need to \(\sum\) IMS from these 6 REGIONs to calculate SHAREs.

#Keep IMS from CRs (6)
IMS.FDATA <- IMS.DATA %>% filter(Residence == "WORLD", BirthCode %in% c(903, 935, 908, 904, 905, 909))

#Make ∑/Year
IMS.FDATA_CRs <- IMS.FDATA %>% group_by(Year) %>% 
  summarize(IMS_Total_CRs = sum(IMS_Total), IMS_Male_CRs = sum(IMS_Male), IMS_Female_CRs = sum(IMS_Female))

#Check Correct ∑
IMS.DATA_World <- IMS.DATA %>% filter(Residence == "WORLD", Birth == "WORLD")

#∑/Year !Correct => Omitted DATA
IMS.FDATA_CRs <- merge(IMS.FDATA_CRs, IMS.DATA_World, by = "Year") %>% 
  rename(IMS_Total_World = IMS_Total, IMS_Male_World = IMS_Male, IMS_Female_World = IMS_Female) %>%
  mutate(Omitted_Total_SHARE = round(((IMS_Total_World - IMS_Total_CRs) / IMS_Total_World) * 100, 3),
         Omitted_Male_SHARE = round(((IMS_Male_World - IMS_Male_CRs) / IMS_Male_World) * 100, 3),
         Omitted_Female_SHARE = round(((IMS_Female_World - IMS_Female_CRs) / IMS_Female_World) * 100, 3)) %>% 
  select(5:8, 1, 9, 2, 12, 10, 3, 13, 11, 4, 14)

#Table World-World
datatable(IMS.FDATA_CRs[, c(1, 3, 5:14)], options = list(scrollX = TRUE, autoWidth = TRUE), 
          style = "bootstrap", rownames = FALSE, caption = "World's Total MiGration Stocks (1990-2020)") %>%
  formatStyle(columns = c(1, 2), textAlign = "left") %>% formatStyle(columns = c(3:14), textAlign = "center")

IMS_Total_CRs \(<\) World-World IMS (IMS_Total_World). There is 4-5.5% omitted data (Omitted_Total_SHARE) in IMS_Total_CRs. Where are these 4-5.5%? XLSX file include a row with IMS born in Other for countries but not for REGIONs, so we \(\sum\) all these Other rows (IMS_Total_OTHER). IMS_Total_OTHER match Omitted_Total_SHARE?

#Keep Other/Countries
IMS.DATA_OTHERs <- IMS.DATA %>% filter(Birth == "Other")

#∑/Year
IMS.DATA_OTHER <- IMS.DATA_OTHERs %>% group_by(Year) %>%
  summarize(IMS_Total_OTHER = sum(IMS_Total), IMS_Male_OTHER = sum(IMS_Male), IMS_Female_OTHER = sum(IMS_Female))

#Variable(s)...
IMS.DATA_OTHER <- IMS.DATA_OTHER %>% 
  mutate(
    Residence = IMS.FDATA_CRs$Residence, 
    ResidenceCode = IMS.FDATA_CRs$ResidenceCode,
    Birth = "OTHER", BirthCode = 2003) %>% select(5:8, everything())

#Table World-OTHER
kable(IMS.DATA_OTHER[ , c(1, 3, 5:8)], row.names = FALSE, 
      align = c("l", "l", "c", "c", "c", "c"), 
      caption = "World's MiGration Stocks born in OTHER (1990-2020)")

World’s MiGration Stocks born in OTHER (1990-2020)
Residence	Birth	Year	IMS_Total_OTHER	IMS_Male_OTHER	IMS_Female_OTHER
WORLD	OTHER	1990	8640334	4408990	4231344
WORLD	OTHER	1995	7477497	3776224	3701273
WORLD	OTHER	2000	7063730	3593958	3469772
WORLD	OTHER	2005	7820665	4096864	3723801
WORLD	OTHER	2010	8757236	4601492	4155744
WORLD	OTHER	2015	10918842	5685512	5233330
WORLD	OTHER	2020	12657151	6533198	6123953

IMS_Total_World = IMS_Total_CRs + IMS_Total_OTHER. We can include a seventh REGION called OTHER to make our visualization. Now, we are able to calculate SHAREs for REGIONs (IMS_Total_SHARE).

#DATA MANIPULATION...
IMS.DATA_OTHER <- IMS.DATA_OTHER %>% 
  rename(IMS_Total = IMS_Total_OTHER, IMS_Male = IMS_Male_OTHER, IMS_Female = IMS_Female_OTHER) 
IMS.FDATA <- rbind(IMS.FDATA, IMS.DATA_OTHER)

#Calculate SHARE/IMS_Total
SHAREs <- list()
for (ROW in 1:nrow(IMS.FDATA)) {
  YYYY <- IMS.FDATA$Year[ROW]
  IMS <- IMS.FDATA_CRs$IMS_Total_World[IMS.FDATA_CRs$Year == YYYY]
  SHARE <- round((IMS.FDATA$IMS_Total[ROW] / IMS) * 100, 5)
  SHAREs[[ROW]] <- SHARE}
IMS.FDATA$IMS_Total_SHARE <- unlist(SHAREs)

#Calculate SHARE/IMS_Male
SHAREs <- list()
for (ROW in 1:nrow(IMS.FDATA)) {
  YYYY <- IMS.FDATA$Year[ROW]
  IMS <- IMS.FDATA_CRs$IMS_Male_World[IMS.FDATA_CRs$Year == YYYY]
  SHARE <- round((IMS.FDATA$IMS_Male[ROW] / IMS) * 100, 5)
  SHAREs[[ROW]] <- SHARE}
IMS.FDATA$IMS_Male_SHARE <- unlist(SHAREs)

#Calculate SHARE/IMS_Female
SHAREs <- list()
for (ROW in 1:nrow(IMS.FDATA)) {
  YYYY <- IMS.FDATA$Year[ROW]
  IMS <- IMS.FDATA_CRs$IMS_Female_World[IMS.FDATA_CRs$Year == YYYY]
  SHARE <- round((IMS.FDATA$IMS_Female[ROW] / IMS) * 100, 5)
  SHAREs[[ROW]] <- SHARE}
IMS.FDATA$IMS_Female_SHARE <- unlist(SHAREs)

#Table World-(Europe and NA)
kable(IMS.FDATA[15:21, c(1, 3, 5:11)], row.names = FALSE, 
      align = c("l", "l", "c", "c", "c", "c", "c", "c", "c"),
      caption = "World's MiGration Stocks (%) born in EUROPE (1990-2020)", 
      digits = c(0, 0, 0, 0, 0, 0, 3, 3, 3))

World’s MiGration Stocks (%) born in EUROPE (1990-2020)
Residence	Birth	Year	IMS_Total	IMS_Male	IMS_Female	IMS_Total_SHARE	IMS_Male_SHARE	IMS_Female_SHARE
WORLD	EUROPE	1990	47730278	22438898	25291380	31.199	28.930	33.533
WORLD	EUROPE	1995	48636003	22816174	25819829	30.154	27.967	32.393
WORLD	EUROPE	2000	49727784	23247653	26480131	28.706	26.502	30.967
WORLD	EUROPE	2005	51336984	24061192	27275792	26.815	24.637	29.084
WORLD	EUROPE	2010	54711301	25508453	29202848	24.758	22.387	27.282
WORLD	EUROPE	2015	56577753	26369327	30208426	22.817	20.563	25.232
WORLD	EUROPE	2020	63273663	29748898	33524765	22.550	20.424	24.844

IMS_Total_SHARE in World born in EUROPE is down, from 31.20% in 1990 to 22.55% in 2020.

To visualize IMS DATA (STOCKs and SHAREs), we will use 4 libraries (ggplot2 | ggplotly | ggiraph | plotly).

3.2.1 Evolution

We will use LINEs and STACKEd BARs to illustrate IMS Evolution (in Millions) from 1990 to 2020. STACKEd BARs will show IMS Evolution in a Residence Area from \(n\) Birth Areas.

3.2.1.1 LINEs

#N°1
LINEs <- ggplot(IMS.FDATA_CRs, aes(x = Year)) +
  geom_line(aes(y = IMS_Total_World, color = "IMS_Total_World", group = "IMS_Total_World")) +
  geom_line(aes(y = IMS_Male_World, color = "IMS_Male_World", group = "IMS_Male_World")) +
  geom_line(aes(y = IMS_Female_World, color = "IMS_Female_World", group = "IMS_Female_World")) +
  geom_point(aes(y = IMS_Total_World, color = "IMS_Total_World", group = "IMS_Total_World")) +
  geom_point(aes(y = IMS_Male_World, color = "IMS_Male_World", group = "IMS_Male_World")) +
  geom_point(aes(y = IMS_Female_World, color = "IMS_Female_World", group = "IMS_Female_World")) +
  labs(title = "International MiGrant Stocks in World (1990-2020)", y = "International MiGrant Stock") +
  #200 000 To 200M
  scale_y_continuous(labels = scales::label_number(scale = 1e-6, suffix = "M")) +
  theme_minimal() +
  scale_color_manual(
    values = c("IMS_Total_World" = "black", "IMS_Male_World" = "blue", "IMS_Female_World" = "pink"), 
    labels = c(
      "IMS_Total_World" = "Both Sexes Combined", "IMS_Male_World" = "Males",  "IMS_Female_World" = "Females")) +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "top", legend.title = element_blank())
LINEs

We can follow IMS Evolution, which has become more intense since 2005. STOCKs are balanced in a fair manner between males and females (1990-2005), then leans in favor of men (2005-2020).

We don’t have accurate STOCKs because it’s static, we need to make it interactive. We also have to rescale STOCKs to add “M” suffix on YAxis.

#N°2
LINEs_INTERACTIVE_One <- plot_ly(data = IMS.FDATA_CRs, x = ~Year, y = ~IMS_Total_World, 
                             type = 'scatter', name = 'Both Sexes Combined', 
                             mode = 'lines+markers', line = list(color = 'black'), marker = list(color = 'black'), 
                             text = with(IMS.FDATA_CRs, paste(" Residence:", Residence, "<br>",
                                                              "Birth:", Birth, "<br>", 
                                                              "Year:", Year, "<br>",
                                                              "International MiGrant Stock (Both Sexes Combined):", 
                                                              paste0(format(x = IMS_Total_World, trim = TRUE, 
                                                                            scientific = FALSE, big.mark = " "), "M"))), 
                             hoverinfo = "text") %>%
   add_trace(x = ~Year, y = ~IMS_Male_World, name = 'Males', line = list(color = 'blue'), marker = list(color = 'blue'), 
             text = with(IMS.FDATA_CRs, paste(" Residence:", Residence, "<br>", "Birth:", Birth, "<br>", "Year:", Year, "<br>", 
                                              "International MiGrant Stock (Males):", 
                                              paste0(format(x = IMS_Male_World, trim = TRUE, scientific = FALSE, 
                                                            big.mark = " "), "M"))), hoverinfo = "text") %>%
   add_trace(x = ~Year, y = ~IMS_Female_World, name = 'Females', line = list(color = 'pink'), marker = list(color = 'pink'), 
             text = with(IMS.FDATA_CRs, paste(" Residence:", Residence, "<br>", "Birth:", Birth, "<br>", "Year:", Year, "<br>", 
                                              "International MiGrant Stock (Females):", 
                                              paste0(format(x = IMS_Female_World, trim = TRUE, scientific = FALSE, 
                                                            big.mark = " "), "M"))), hoverinfo = "text") %>% 
  layout(title = "International MiGrant Stocks in World (1990-2020)", 
         xaxis = list(title = "Year"), yaxis = list(title = "International MiGrant Stock"),
         legend = list(x = 0.5, y = 1.05, xanchor = "center", orientation = "h"))
LINEs_INTERACTIVE_One

A download button is available. We can see Residence | Birth | Year | STOCKs in Hover Text. We don’t need to rescale STOCKs to add “M” suffix on YAxis, we need to do it for Hover Text.

#N°3
LINEs_INTERACTIVE_Two <- ggplotly(LINEs) %>% 
  layout(legend = list(title = "", x = 0.5, y = 1.05, xanchor = "center", orientation = "h"))
for (i in 1:length(LINEs_INTERACTIVE_Two$x$data)) {
  LINEs_INTERACTIVE_Two$x$data[[i]]$name <- c("Both Sexes Combined", "Males", "Females")[i]}
LINEs_INTERACTIVE_Two

We can’t remove default Hover Text and text ARGu within aes() in geom_point() does not allow for modifications to Hover Text. Customs labels have to be redone here.

#N°4
LINEs_INTERACTIVE_THREE <- ggplot(IMS.FDATA_CRs) +
  geom_line_interactive(aes(x = Year, y = IMS_Total_World, color = "IMS_Total_World", group = "IMS_Total_World")) +
  geom_line_interactive(aes(x = Year, y = IMS_Male_World, color = "IMS_Male_World", group = "IMS_Male_World")) +
  geom_line_interactive(aes(x = Year, y = IMS_Female_World, color = "IMS_Female_World", group = "IMS_Female_World")) +
  geom_point_interactive(aes(x = Year, y = IMS_Total_World, color = "IMS_Total_World", group = "IMS_Total_World", 
                             tooltip = paste("Residence:", Residence, "<br>Birth:", Birth, "<br>Year:", Year, 
                                             "<br>International MiGrant Stock (Both Sexes Combined):", 
                                             paste0(format(x = IMS_Total_World, trim = TRUE, 
                                                           scientific = FALSE, big.mark = " "), "M")))) +
  geom_point_interactive(aes(x = Year, y = IMS_Male_World, color = "IMS_Male_World", group = "IMS_Male_World", 
                             tooltip = paste("Residence:", Residence, "<br>Birth:", Birth, "<br>Year:", Year,
                                             "<br>International MiGrant Stock (Males):", 
                                             paste0(format(x = IMS_Male_World, trim = TRUE, 
                                                           scientific = FALSE, big.mark = " "), "M")))) +
  geom_point_interactive(aes(x = Year, y = IMS_Female_World, color = "IMS_Female_World", group = "IMS_Female_World", 
                             tooltip = paste("Residence:", Residence, "<br>Birth:", Birth, "<br>Year:", Year, 
                                             "<br>International MiGrant Stock (Females):", 
                                             paste0(format(x = IMS_Female_World, trim = TRUE,
                                                           scientific = FALSE, big.mark = " "), "M")))) + 
  labs(title = "International MiGrant Stocks in World (1990-2020)", y = "International MiGrant Stock") +
  scale_y_continuous(labels = scales::label_number(scale = 1e-6, suffix = "M")) +
  theme_minimal() +
  scale_color_manual(
    values = c("IMS_Total_World" = "black", "IMS_Male_World" = "blue", "IMS_Female_World" = "pink"), 
    labels = c("IMS_Total_World" = "Both Sexes Combined", "IMS_Male_World" = "Males",  "IMS_Female_World" = "Females")) +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "top", legend.title = element_blank())
girafe(ggobj = LINEs_INTERACTIVE_THREE)

Function geom_bar_interactive serves as an alternative to geom_bar to make it interactive. A download button is also available.

3.2.1.2 STACKEd BARs

#COLORs
CRs <- c("#339900", "#FF9900", "#003366", "#FF0000", "#663399", "#FFCC00", "#999999")

#N°1
BARs <- ggplot(IMS.FDATA, aes(x = Year, y = IMS_Total, fill = Birth, 
                                     #For N°3
                                     text = paste(" Residence:", Residence, "<br>", "Birth:", Birth, "<br>", 
                                                  "Year:", Year, "<br>", "International MiGrant Stock:", 
                                                  format(x = IMS_Total, trim = TRUE, scientific = FALSE, big.mark = " ")))) +
  #STACKEd
  geom_bar(stat = "identity") + 
  labs(title = "International MiGrant Stocks in World from 6 Areas (1990-2020)", 
       y = "International MiGrant Stock - Both Sexes Combined") +
  scale_y_continuous(labels = scales::label_number(scale = 1e-6, suffix = "M")) +
  scale_fill_manual(values = CRs) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "right")
BARs

We can see IMS Global Evolution from 150M in 1990 to \(>\) 250M in 2020. We also notice a more marked evolution in ASIA than in EUROPE.

Birth Areas are in A-Z order, we will have to use factor levels to reorder Birth Areas.

Non-POSITIVE comments are same as for LINES. OCEANIA and NORTHERN AMERICA are too small.

In STACKEd BARs above, we have data for a Residence-Birth pair and all Year in a row.

#Residence-Birth/Year To Year/Residence/Birth
IMS.FDATA_INTERACTIVE <- IMS.FDATA %>% group_by(Year, Residence, Birth) %>% 
  summarise(IMS_Total_SHARE = sum(IMS_Total_SHARE), IMS_Total = sum(IMS_Total))

#Table 1990/World
kable(IMS.FDATA_INTERACTIVE[1:7, c(1:3, 5)], row.names = FALSE, 
      align = c("l", "l", "l", "c"),
      caption = "International MiGrant Stocks in World from 6 Areas in 1990")

International MiGrant Stocks in World from 6 Areas in 1990
Year	Residence	Birth	IMS_Total
1990	WORLD	AFRICA	20621464
1990	WORLD	ASIA	56957000
1990	WORLD	EUROPE	47730278
1990	WORLD	LATIN AMERICA AND THE CARIBBEAN	15273399
1990	WORLD	NORTHERN AMERICA	2802135
1990	WORLD	OCEANIA	961547
1990	WORLD	OTHER	8640334

Now, we need to have data for a Year and all Residence-Birth pairs in a row.

#N°2
BARs_INTERACTIVE_One <- plot_ly(data = IMS.FDATA_INTERACTIVE, x = ~Year, y = ~IMS_Total, 
                                color = ~Birth, type = "bar", colors = CRs, legendgroup = 'Birth', 
                                text = with(IMS.FDATA_INTERACTIVE, paste(" Residence:", Residence, "<br>", 
                                                                         "Birth:", Birth, "<br>", "Year:", Year, "<br>", 
                                                                         "International MiGrant Stock:", 
                                                                         paste0(format(IMS_Total, trim = TRUE, 
                                                                                       scientific = FALSE, big.mark = " ")))),
                                hoverinfo = "text") %>% 
  layout(barmode = "stack", 
         yaxis = list(title = "International MiGrant Stock - Both Sexes Combined"),
         title = "International MiGrant Stocks in  World from 6 Areas (1990-2020)", 
         legend = list(title = list(text='<b> Birth </b>'), x=1, y=0.5))
BARs_INTERACTIVE_One

Birth Areas are in Z-A order, we will have to use factor levels to reorder Birth Areas.

#N°3
BARs_INTERACTIVE_Two <- ggplotly(BARs) %>%
  layout(title = list(text = "International MiGrant Stocks in World From 6 Areas (1990-2020)", x = 0.5))
BARs_INTERACTIVE_Two

#N°4
BARs_INTERACTIVE_THREE <- ggplot(IMS.FDATA) + 
  geom_bar_interactive(aes(x = Year, y = IMS_Total, fill = Birth, 
                           tooltip = paste("Residence:", Residence, "<br>Birth:", Birth, "<br>Year:", Year, 
                                           "<br>International MiGrant Stock:", 
                                           paste0(format(IMS_Total, trim = TRUE, scientific = FALSE, big.mark = " ")))), 
                       position = "stack", stat = "identity") +
  labs(title = "International MiGrant Stocks in World from 6 Areas (1990-2020)", 
       y = "International MiGrant Stock - Both Sexes Combined") +
  scale_fill_manual(values = CRs) + 
  scale_y_continuous(labels = scales::label_number(scale = 1e-6, suffix = "M")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "right")
girafe(ggobj = BARs_INTERACTIVE_THREE)

3.2.2 SHARE(s)

STACKEd BARs will show SHAREs in a Residence Area from \(n\) Birth Areas.

#N°2
BARs_INTERACTIVE_SHAREs_One <- plot_ly(data = IMS.FDATA_INTERACTIVE, x = ~Year, y = ~IMS_Total_SHARE, 
                                       color = ~Birth, type = "bar", colors = CRs, legendgroup = 'Birth', 
                                       #HOVER...
                                       text = with(IMS.FDATA_INTERACTIVE, paste(" Residence:", Residence, "<br>", 
                                                                                "Birth:", Birth, "<br>", "Year:", Year, "<br>", 
                                                                                "International MiGrant Stock:",
                                                                                round(IMS_Total_SHARE, 3), "%")), 
                                       hoverinfo = "text") %>% 
  layout(barmode = "stack", 
         yaxis = list(title = "International MiGrant Stock - Both Sexes Combined (%)"),
         title = "International MiGrant Stocks (%) in World from 6 Areas (1990-2020)", 
         legend = list(title = list(text='<b> Birth </b>'), x=1, y=0.5),
         margin = list(t = 30))
BARs_INTERACTIVE_SHAREs_One

IMS SHAREs are \(\nearrow\) in EUROPE (see section 3.2), \(\searrow\) in LATIN AMERICA AND THE CARIBBEAN, look constant in AFRICA.

#N°4
BARs_INTERACTIVE_SHAREs_THREE <- ggplot(IMS.FDATA) +
  geom_bar_interactive(aes(x = Year, y = IMS_Total_SHARE, fill = Birth, 
                           #HOVER...
                           tooltip = paste("Residence:", Residence, "<br>Birth:", Birth, "<br>Year:", Year, 
                                           "<br>International MiGrant Stock:", round(IMS_Total_SHARE, 3), "%")), 
                       stat = "identity") + 
  labs(title = "International MiGrant Stocks (%) in World from 6 Areas (1990-2020)", 
       y = "International MiGrant Stock - Both Sexes Combined (%)") +
  scale_fill_manual(values = CRs) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "right")
girafe(ggobj = BARs_INTERACTIVE_SHAREs_THREE)

3.2.3 Evolution and SHARE(s)

Combine STOCKs and SHAREs thanks to subplot().

#N°2 (x2)
BARs_INTERACTIVE_One <- BARs_INTERACTIVE_One  %>% 
  layout(annotations = list(x = 0.5, y = 1.05, xref = "paper", yref = "paper", 
                            text = "International MiGrant Stocks in World from 6 Areas (1990-2020)", showarrow = FALSE))
BARs_INTERACTIVE_SHAREs_One <- BARs_INTERACTIVE_SHAREs_One %>%
  layout(title = "",
         annotations = list(x = 0.5, y = 1.05, xref = "paper", yref = "paper", 
                            text = "International MiGrant Stocks (%) in World from 6 Areas (1990-2020)", showarrow = FALSE))
BARs_INTERACTIVE_STOCKs_SHAREs_One <- subplot(BARs_INTERACTIVE_One, BARs_INTERACTIVE_SHAREs_One, 
                                              nrows = 2, heights = c(0.5, 0.5),
                                              titleX = TRUE, titleY = TRUE, shareX = TRUE) %>%
  layout(showlegend = TRUE)
BARs_INTERACTIVE_STOCKs_SHAREs_One

We can allow a SHARE x-axis. We have labels twice and have to force to have Y-axis for STOCKs and SHAREs.

Remarks:

Combine static STOCKs and SHAREs with grid.arrange(). We can have one labels
For ggiraph, it’s not doable
Combination is less flexible in Application

3.2.4 Resume

We will use plotly or ggplot2 combine with ggiraph to make our STACKEd BARs and LINEs.

#LibrariesCOMPARISON
LibrariesCOMPARISON <- data.frame(
  LIBRARY = c("GGPLOT2", "PLOTLY", "GGPLOTLY", "GGIRAPH"),
  POSITIVE = c("EASY | Nice GRAPHICs", 
               "Interactive | DownLoad Button | Hover Text | Share X-axis", 
               "Interactive | DownLoad Button", 
               "Interactive | DownLoad Button | Hover Text | EASY"), 
  NEGATIVE = c("Static", "Labels Issues", "Labels Issues | Hover Text", "-"))

#Table
datatable(LibrariesCOMPARISON, options = list(scrollX = TRUE, autoWidth = TRUE), 
          style = "bootstrap", rownames = FALSE, caption = "GRAPHICs' Libraries COMPARISON ")

3.3 DATA MANIPULATIONs

DATA MANIPULATIONs can take time. We need to do it to avoid load times on client/server side.

3.3.1 Dev. Levels

There are 3 Dev. Levels from OHRLLS in IMS DATA (see section 3):

Least Developed Countries (LDC)
Land-Locked DevelopinG Countries (LLDC)
Small Island DevelopinG States (SIDS)

We have found method. data from UNSD which lists countries as LDC| LLDC | SIDS. There are 3 variables (LDC | LLDC | SIDS), with a small cross (x) if an area is in one level (an area can be in 2 levels at once). We create a new variable with 6 modalities:

LDC* | LLDC* | SIDS* | LDC/LLDC | LDC/SIDS | Not in this Classification

We have rows based on LDC | LLDC | SIDS in IMS DATA. We want to create new ones to retrieve data that follow our new variable (6 modalities):

LDC for LDC* and LDC+LLDC for LDC/LLDC \(\Rightarrow\) LDC DATA will be included twice in IMS DATA

We need to calculate new rows thanks to countries data. We have method. data from UNSD (2023) with LDC (46) | LLDC (32) | SIDS (53), and we know LDC (47) | LLDC (32) | SIDS (58) in method. from IMS DATA (2020):

VANUATU ceased to be a LDC in December 2020 (46 To 47) (Sources: 1 | 2)
MARTINIQUE, TURKS AND CAICOS ISLANDS, GUADELOUPE, CAYMAN ISLANDS and BERMUDA (53 To 58) were not included in method. data from UNSD (Source)
BAHRAIN is also a SIDS since we need it to retrieve the correct data thanks to countries data (58 To 59)